-
Zhang, J.; Zeng, M.L.: ¬A new similarity measure for subject hierarchical structures (2014)
0.03
0.03128866 = product of:
0.06257732 = sum of:
0.06257732 = sum of:
0.027226217 = weight(_text_:systems in 1778) [ClassicSimilarity], result of:
0.027226217 = score(doc=1778,freq=2.0), product of:
0.16037072 = queryWeight, product of:
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.052184064 = queryNorm
0.1697705 = fieldWeight in 1778, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.0390625 = fieldNorm(doc=1778)
0.0353511 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
0.0353511 = score(doc=1778,freq=2.0), product of:
0.1827397 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.052184064 = queryNorm
0.19345059 = fieldWeight in 1778, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1778)
0.5 = coord(1/2)
- Abstract
- Purpose - The purpose of this paper is to introduce a new similarity method to gauge the differences between two subject hierarchical structures. Design/methodology/approach - In the proposed similarity measure, nodes on two hierarchical structures are projected onto a two-dimensional space, respectively, and both structural similarity and subject similarity of nodes are considered in the similarity between the two hierarchical structures. The extent to which the structural similarity impacts on the similarity can be controlled by adjusting a parameter. An experiment was conducted to evaluate soundness of the measure. Eight experts whose research interests were information retrieval and information organization participated in the study. Results from the new measure were compared with results from the experts. Findings - The evaluation shows strong correlations between the results from the new method and the results from the experts. It suggests that the similarity method achieved satisfactory results. Practical implications - Hierarchical structures that are found in subject directories, taxonomies, classification systems, and other classificatory structures play an extremely important role in information organization and information representation. Measuring the similarity between two subject hierarchical structures allows an accurate overarching understanding of the degree to which the two hierarchical structures are similar. Originality/value - Both structural similarity and subject similarity of nodes were considered in the proposed similarity method, and the extent to which the structural similarity impacts on the similarity can be adjusted. In addition, a new evaluation method for a hierarchical structure similarity was presented.
- Date
- 8. 4.2015 16:22:13
-
Zhang, J.: Archival context, digital content, and the ethics of digital archival representation : the ethics of identification in digital library metadata (2012)
0.01
0.009625921 = product of:
0.019251842 = sum of:
0.019251842 = product of:
0.038503684 = sum of:
0.038503684 = weight(_text_:systems in 419) [ClassicSimilarity], result of:
0.038503684 = score(doc=419,freq=4.0), product of:
0.16037072 = queryWeight, product of:
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.052184064 = queryNorm
0.24009174 = fieldWeight in 419, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.0390625 = fieldNorm(doc=419)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The findings of a recent study on digital archival representation raise some ethical concerns about how digital archival materials are organized, described, and made available for use on the Web. Archivists have a fundamental obligation to preserve and protect the authenticity and integrity of records in their holdings and, at the same time, have the responsibility to promote the use of records as a fundamental purpose of the keeping of archives (SAA 2005 Code of Ethics for Archivists V & VI). Is it an ethical practice that digital content in digital archives is deeply embedded in its contextual structure and generally underrepresented in digital archival systems? Similarly, is it ethical for archivists to detach digital items from their archival context in order to make them more "digital friendly" and more accessible to meet needs of some users? Do archivists have an obligation to bring the two representation systems together so that the context and content of digital archives can be better represented and archival materials "can be located and used by anyone, for any purpose, while still remaining authentic evidence of the work and life of the creator"? (Millar 2010, 157) This paper discusses the findings of the study and their ethical implications relating to digital archival description and representation.
-
Zhuge, H.; Zhang, J.: Topological centrality and its e-Science applications (2010)
0.01
0.009529176 = product of:
0.019058352 = sum of:
0.019058352 = product of:
0.038116705 = sum of:
0.038116705 = weight(_text_:systems in 3984) [ClassicSimilarity], result of:
0.038116705 = score(doc=3984,freq=2.0), product of:
0.16037072 = queryWeight, product of:
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.052184064 = queryNorm
0.23767869 = fieldWeight in 3984, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.0546875 = fieldNorm(doc=3984)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Network structure analysis plays an important role in characterizing complex systems. Different from previous network centrality measures, this article proposes the topological centrality measure reflecting the topological positions of nodes and edges as well as influence between nodes and edges in general network. Experiments on different networks show distinguished features of the topological centrality by comparing with the degree centrality, closeness centrality, betweenness centrality, information centrality, and PageRank. The topological centrality measure is then applied to discover communities and to construct the backbone network. Its characteristics and significance is further shown in e-Science applications.
-
Liu, X.; Zhang, J.; Guo, C.: Full-text citation analysis : a new method to enhance scholarly networks (2013)
0.01
0.0068065543 = product of:
0.013613109 = sum of:
0.013613109 = product of:
0.027226217 = sum of:
0.027226217 = weight(_text_:systems in 1044) [ClassicSimilarity], result of:
0.027226217 = score(doc=1044,freq=2.0), product of:
0.16037072 = queryWeight, product of:
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.052184064 = queryNorm
0.1697705 = fieldWeight in 1044, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.0390625 = fieldNorm(doc=1044)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.
